Modeling and Forecasting Cryptocurrency Closing Prices with Rao Algorithm-Based Artificial Neural Networks: A Machine Learning Approach

Sanjib Kumar Nayak, Sarat Chandra Nayak and Subhranginee Das
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Sanjib Kumar Nayak: Department of Computer Application, VSS University of Technology, Burla, Sambalpur 768018, India
Sarat Chandra Nayak: Department of Artificial Intelligence and Machine Learning, CMR College of Engineering & Technology, Hyderabad 501401, India
Subhranginee Das: Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation (KL University), Hyderabad 500075, India

FinTech, 2021, vol. 1, issue 1, 1-16

Abstract: Artificial neural networks (ANNs) are suitable procedures for predicting financial time series (FTS). Cryptocurrencies are good investment assets; therefore, the effective prediction of cryptocurrencies has become a trending area of research. Capturing inherent uncertainties associated with cryptocurrency FTS with conventional methods is difficult. Though ANNs are the better alternative, fixing the optimal parameters of ANNs is a tedious job. This article develops a hybrid ANN through Rao algorithm (RA + ANN) for the effective prediction of six popular cryptocurrencies such as Bitcoin, Litecoin, Ethereum, CMC 200, Tether, and Ripple. Six comparative models such as GA + ANN, PSO + ANN, MLP, SVM, LSE, and ARIMA are developed and trained in a similar way. All these models are evaluated through the mean absolute percentage of error (MAPE) and average relative variance (ARV) metrics. It is found that the proposed RA + ANN generated the lowest MAPE and ARV values, statistically different as compared with existing methods mentioned above, and hence can be recommended as a potential financial instrument for predicting cryptocurrencies.

Keywords: cryptocurrency; Bitcoin; artificial neural network; financial forecasting; Rao algorithm; multilayer perceptron; cryptocurrency prediction (search for similar items in EconPapers)
JEL-codes: C6 F3 G O3 (search for similar items in EconPapers)
Date: 2021
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